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Chaotic analysis of predictability versus knowledge discovery techniques: case study of the Polish stock market

Authors
Chun, SHKim, KJKim, SH
Issue Date
Nov-2002
Publisher
BLACKWELL PUBL LTD
Keywords
chaotic models; knowledge discovery; backpropagation neural network; case-based reasoning
Citation
EXPERT SYSTEMS, v.19, no.5, pp 264 - 272
Pages
9
Journal Title
EXPERT SYSTEMS
Volume
19
Number
5
Start Page
264
End Page
272
URI
https://scholarworks.sookmyung.ac.kr/handle/2020.sw.sookmyung/16383
DOI
10.1111/1468-0394.00213
ISSN
0266-4720
1468-0394
Abstract
Increasing evidence over the past decade indicates that financial markets exhibit nonlinear dynamics in the form of chaotic behavior. Traditionally, Ate prediction of stock markets has relied on statistical methods including multivariate statistical methods, autoregressive integrated moving average models and autoregressive conditional heteraskedasticity models. In recent yearsy neural networks and other knowledge techniques have been applied extensively to the task of predicting financial variables. This paper examines the relationship between chaotic models and teaming techniques. In particular, chaotic analysis indicates the ripper limits of predictability for a time series. The teaming techniques involve neural networks and case-based reasoning. The chaotic models take the form of R/S analysis to measure persistence in a time series, the correlation dimension to encapsulate system complexity and Lyapunov exponents to indicate predictive horizons. The concepts are illustrated in the context of a major emerging market, namely the Polish stock market.
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